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Interpreted broadly, data is any storable information that helps to define a situation. This means data could be coordinates, it could be the starting and stopping points that determine a span of time, it could be content that is made into a video. Data is a byproduct of life. Data happens when life happens; it is just a question of collecting and using it.

Technology solves problems and generally improves things because it processes, transports, or otherwise manipulates data. The human usability of technology has made significant advances of late precisely because the technology needed to meaningfully convert data into something human usable has become more transportable, more powerful, and easier to use than ever. The technology is what you hold in your hand, but it’s the data that makes you think.

We use data to figure stuff out. It is what can be deployed to convert a hunch into a probability. When we see the kinds of data display and data thinking that drive such sites as Jim Davenport’s IfWeAssume.com, Visual.ly or any o the others it helps reinforce the message that people and companies and brands and organizations and governments need to pay attention to data. They may not always like what the data tells them, but all parties need to know “what really happens when” to understand and – if desired – change behavior.

Consider if a data collection effort was launched around smoking with the objective being to determine where and what time of day people normally lit up. Regardless of whether the results would surprise us or fall right into line with our expectations we would learn where and when to place anti-smoking messaging to have maximum exposure. We would be better able to tune the messages to the milieu, and thus have a better shot at improving the campaign’s impact. We could then create not a message or a campaign – too short-lived – but an entire narrative on the subject that gets at the heart of where and when the behavior happens so, perhaps, that behavior can be altered.

Passport Renewal, Two Books of Stamps and a Grande No-Foam Pumpkin-Spice Latte, PleaseIn my exchange with Phil I opined about making Starbucks (SBUX for my homies who play the markets) the new USPS. This momentary flight of the imagination was launched by the mapping that Mr. Davenport did on his blog, which Phil introduced me to. It was how the data was expressed that made me think.

Given Starbucks’ distribution and how Mr. Davenport related it to US population density, I overlaid the notional data I have in my head about the relative failure of the USPS to operate efficiently, it seemed momentarily worthy to consider combining the two organizations. I wondered aloud what a similar map would look like for the USPS: Would the USPS map look more evenly distributed than the SBUX map? Probably, and that’s the problem: Starbucks is efficient, the USPS is not.

We are all aware of the issues facing the USPS, but the main problem must be its forced stewardship of an impossible conceit: that every US resident receives the same service for the same price no matter what the actual cost of service is. Starbucks’ own pricing is largely uniform, too. But SBUX is not compelled by law to be uniform, and they are allowed to locate – and not locate – wherever they think it best. Neither do they have the cost structure associated with sorting infrastructure and the fleet of vehicles for delivery. Finally, your neighborhood barista is non-union.

All that said, closing the USPS and awarding SBUX a contract to receive mail and service those who want to send packages is clearly ridiculous. But that’s not the point. What matters is that data gives us the opportunity to explore all routes, all options, all potential solutions. By having the data and expressing it in the right way, the data becomes a story, a path, a direction. Decision-makers have something to guide their choices.

For so many decades the gathering and analysis of data was hard. It took time and cost a lot of money. This tended to concentrate the act of data collection and analysis around larger organizations that – very much like the USPS – could build the massive infrastructure needed to do the job. It is only in the last 15 years – and particularly the last five – that the handling, organization and application of data has become so cheap and so easy. As Jim Davenport and so many others show us every day, when you take the time to visualize your data, you can start to visualize your future.*

* – Irony alert: Jim Davenport is a PhD candidate in the Astronomy Department at University of Washington. So his primary interest is, in fact, to look very deeply into the past.